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为解决大量复杂凹形障碍环境中的路径规划问题,采用了计算机仿真技术,对双蚁群完全交叉算法进行了研究。通过对传统蚁群算法增加新型的距离改变启发因子,建立双蚁群完全交叉算法,并且融入最大最小蚁群算法思想,使蚁群算法应用在机器人路径规划领域,即使机器人环境中有大量复杂的凹形障碍,该算法仍能够规划出高质量的路径。仿真试验表明该算法得到最优路径率达到98%。
To solve the problem of path planning in a large number of complex concave obstacle environments, a computer simulation technique was used to study the complete crossover algorithm of double ant colony. By adding a new type of distance to the traditional ant colony algorithm and changing the heuristic factor, a complete crossover algorithm of double ant colony is established and the idea of maximum and minimum ant colony algorithm is established to make the ant colony algorithm applied in the field of robot path planning. Even though there are a large number of complicated Concave obstacles, the algorithm is still able to plan a high-quality path. Simulation results show that the algorithm achieves the optimal path rate of 98%.